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/*=========================================================================
*
* Copyright Insight Software Consortium
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0.txt
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
*=========================================================================*/
#include <fstream>
#include "itkPointSetToListSampleAdaptor.h"
#include "itkWeightedCentroidKdTreeGenerator.h"
#include "itkKdTreeBasedKmeansEstimator.h"
int itkKdTreeBasedKmeansEstimatorTest(int argc, char* argv[] )
{
namespace stat = itk::Statistics;
if (argc < 4)
{
std::cerr << "Missing Arguments" << std::endl;
std::cerr << "Usage: " << std::endl;
std::cerr << argv[0] << "inputFileName bucketSize minStandardDeviation tolerancePercent" << std::endl;
return EXIT_FAILURE;
}
unsigned int i;
unsigned int j;
char* dataFileName = argv[1];
int dataSize = 2000;
int bucketSize = atoi( argv[3] );
double minStandardDeviation = atof( argv[2] );
itk::Array< double > trueMeans(4);
trueMeans[0] = 99.261;
trueMeans[1] = 100.078;
trueMeans[2] = 200.1;
trueMeans[3] = 201.3;
itk::Array< double > initialMeans(4);
initialMeans[0] = 80.0;
initialMeans[1] = 80.0;
initialMeans[2] = 180.0;
initialMeans[3] = 180.0;
int maximumIteration = 200;
/* Loading point data */
typedef itk::PointSet< double, 2 > PointSetType;
PointSetType::Pointer pointSet = PointSetType::New();
PointSetType::PointsContainerPointer pointsContainer =
PointSetType::PointsContainer::New();
pointsContainer->Reserve(dataSize);
pointSet->SetPoints(pointsContainer.GetPointer());
PointSetType::PointsContainerIterator p_iter = pointsContainer->Begin();
PointSetType::PointType point;
double temp;
std::ifstream dataStream(dataFileName);
while (p_iter != pointsContainer->End())
{
for ( i = 0; i < PointSetType::PointDimension; i++)
{
dataStream >> temp;
point[i] = temp;
}
p_iter.Value() = point;
++p_iter;
}
dataStream.close();
/* Importing the point set to the sample */
typedef stat::PointSetToListSampleAdaptor< PointSetType >
DataSampleType;
DataSampleType::Pointer sample =
DataSampleType::New();
sample->SetPointSet(pointSet);
/* Creating k-d tree */
typedef stat::WeightedCentroidKdTreeGenerator< DataSampleType > Generator;
Generator::Pointer generator = Generator::New();
generator->SetSample(sample.GetPointer());
generator->SetBucketSize(bucketSize);
generator->GenerateData();
/* Searching kmeans */
typedef stat::KdTreeBasedKmeansEstimator< Generator::KdTreeType > Estimator;
Estimator::Pointer estimator = Estimator::New();
std::cout << estimator->GetNameOfClass() << std::endl;
estimator->Print( std::cout );
//Set the initial means
estimator->SetParameters(initialMeans);
//Set the maximum iteration
estimator->SetMaximumIteration(maximumIteration);
if ( estimator->GetMaximumIteration() != maximumIteration )
{
std::cerr << "Error in Set/GetMaximum Iteration" << std::endl;
return EXIT_FAILURE;
}
estimator->SetKdTree(generator->GetOutput());
//Set the centroid position change threshold
estimator->SetCentroidPositionChangesThreshold(0.0);
const double tolerance = 0.1;
if( std::fabs(estimator->GetCentroidPositionChangesThreshold() - 0.0) > tolerance )
{
std::cerr << "Set/GetCentroidPositionChangesThreshold() " << std::endl;
return EXIT_FAILURE;
}
estimator->StartOptimization();
Estimator::ParametersType estimatedMeans = estimator->GetParameters();
bool passed = true;
int index;
const unsigned int numberOfMeasurements = sample->GetMeasurementVectorSize();
const unsigned int numberOfClasses = trueMeans.size() / numberOfMeasurements;
for (i = 0; i < numberOfClasses; i++)
{
std::cout << "cluster[" << i << "] " << std::endl;
double displacement = 0.0;
std::cout << " true mean :" << std::endl;
std::cout << " ";
index = numberOfMeasurements * i;
for (j = 0; j < numberOfMeasurements; j++)
{
std::cout << trueMeans[index] << " ";
++index;
}
std::cout << std::endl;
std::cout << " estimated mean :" << std::endl;
std::cout << " ";
index = numberOfMeasurements * i;
for (j = 0; j < numberOfMeasurements; j++)
{
std::cout << estimatedMeans[index] << " ";
temp = estimatedMeans[index] - trueMeans[index];
++index;
displacement += (temp * temp);
}
std::cout << std::endl;
displacement = std::sqrt(displacement);
std::cout << " Mean displacement: " << std::endl;
std::cout << " " << displacement
<< std::endl << std::endl;
double tolearancePercent = atof( argv[3] );
// if the displacement of the estimates are within tolearancePercent% of
// standardDeviation then we assume it is successful
if( displacement > ( minStandardDeviation * tolearancePercent ) )
{
std::cerr << "displacement is larger than tolerance ";
std::cerr << minStandardDeviation * tolearancePercent << std::endl;
passed = false;
}
}
if( !passed )
{
std::cout << "Test failed." << std::endl;
return EXIT_FAILURE;
}
std::cout << "Test passed." << std::endl;
return EXIT_SUCCESS;
}
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